What this category of data looks like
This category of data comes from the unified budget management system, centralized treasury payment system, accounting system, and asset management system. Its update frequency is annual. Commonly processed documents include department final account statements, balance sheets, income and expenditure final account statements, final account explanations, and government financial reports. These documents cover core fields such as actual revenue and expenditure, assets and liabilities, and budget execution differences. Units mostly use financial measurement standards such as ten thousand yuan and yuan. Most document structures combine structured tables and text descriptions.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
The annual update feature requires multi-turn dialogue to adapt to context loading of batch historical data. This avoids overloading a single context and affecting review efficiency. Data from multiple systems requires prompts to clearly align field standards across different systems. This prevents review errors caused by data confusion. The mixed document structure of structured reports and text descriptions requires prompts to distinguish task boundaries between table data verification and text difference explanation. The large data volume of a single year limits the context length that can be loaded in a single dialogue. Data batches must be reasonably split to ensure complete content.
How to set the configuration
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
messages | Split context so that each single dialogue turn does not exceed 8000 characters | Department final account documents have large single-year data volumes. This avoids data truncation caused by exceeding context limits, and adapts to the context loading rules of FastGPT v4.17.0 |
variables | Preset three variables: report caliber alignment, difference explanation, and subordinate report summary | Matches the task types of the core review links of this category, and simplifies the prompt calling logic for multi-turn dialogue |
| Maximum context length | 6000–10000 characters | Adapts to the mixed structure of structured reports and text descriptions in annual final account documents, balancing context completeness and loading speed |
| Single dialogue return limit | Top 3 matching results | This category requires accurate reconciliation of account statements and treasury data, to avoid redundant information interfering with review judgments |
chatId | Bind to annual final account batch ID | Distinguishes review sessions for different years, retains context association for multi-turn dialogue, and avoids batch data confusion |
| Dialogue timeout duration | 300–600 seconds | This category of review tasks requires processing multi-system data comparison, reserving sufficient response processing time |
Three Common Mistakes
- Phenomenon: The frontend calls
http://localhost:3000/api/v1/chat/completionsand returns a CORS error with a status code of 403. Cause: No cross-origin allowed domain whitelist is configured in the FastGPT deployment environment, or the interface does not have cross-origin access permissions enabled. - Phenomenon: Accurate token consumption statistics cannot be obtained after the dialogue ends. Cause: Token tracking configuration for session logs is not enabled, so token usage for each dialogue turn cannot be recorded.
- Phenomenon: After importing a Word document with 100,000 Chinese characters or an Excel dataset with more than 15,000 rows, the dialogue returns no results. Cause: Large-volume documents are not split into batches that conform to the context length limit, resulting in incomplete data loading.
How to Confirm Proper Configuration
- Call the dialogue interface, then check the returned
messagesfield to confirm that the loaded final account data matches the currently bound annual batch. - Test importing a single-year final account document, and check that the context is complete during the dialogue process with no obvious field omissions.
- Enable session logs, and verify the token consumption records for each dialogue turn to confirm that the statistics logic works properly.
- After configuring the cross-origin whitelist, use a frontend testing tool to send a request and confirm that no CORS error occurs.
The values listed here are common starting points, and should be assessed against samples used in the actual deployment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.